Installing and Launching R Packages
packages = c('raster','sf','tmap','tidyverse','clock','rgdal','tidytext','widyr',
'wordcloud','DT','ggwordcloud','textplot','lubridate','dplyr','hms',
'tidygraph','ggraph','igraph','wordcloud2','hms','stringi','mapview',
'data.table','crosstalk')
for(p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
Read credit card details
cc <- read.csv(file = 'data/cc_data.csv',encoding="UTF-8")
Read credit card details
lc <- read.csv(file = 'data/loyalty_data.csv',encoding="UTF-8")
Get location frequency for credit cards
wc1 <- cc %>%
count(location, sort=TRUE)
Get location frequency for loyalty cards
wc2 <- lc %>%
count(location, sort=TRUE)
Basic word cloud for credit card transactions
wordcloud(words = wc1$location, freq = wc1$n, min.freq = 1,max.words=200,
random.order=FALSE, rot.per=0,scale=c(2.5,0.5),colors=brewer.pal(8,
"Dark2"),vfont=c("sans serif","plain"), font = 5)
Basic word cloud for loyalty card transactions
wordcloud(words = wc2$location, freq = wc2$n, min.freq = 1,max.words=200,
random.order=FALSE, rot.per=0,scale=c(2.5,0.5),colors=brewer.pal(8,
"Dark2"),vfont=c("sans serif","plain"), font = 5)
Least visited locations based on cc
location n
29 Maximum Iron and Steel 6
30 Abila Scrapyard 4
31 Octavio's Office Supplies 4
32 Frank's Fuel 2
33 U-Pump 2
34 Daily Dealz 1
Least visited locations based on lc
location n
28 Maximum Iron and Steel 6
29 Roberts and Sons 6
30 Abila Scrapyard 4
31 Octavio's Office Supplies 3
32 Frank's Fuel 2
33 U-Pump 1
Split time from date
cc <- cc %>%
separate(timestamp, c("date", "time"), " ")
Find the day of the week
cc$date <- as.Date(cc$date, "%m/%d/%Y")
cc$hour <- as.POSIXct(cc$time, format = "%H:%M")
cc$hour <- as_hms(cc$hour)
cc$hour <- hour(cc$hour)
Generate heatmap - Location vs hour of day
cc$location <- stri_trans_general(cc$location, "latin-ascii")
x_axis_labels <- min(cc[,'hour']):max(cc[,'hour'])
group_by(cc,hour,location) %>% summarize(n=n()) %>%
ggplot(aes(hour,location,fill=n)) + geom_tile() +
scale_x_continuous(expand=c(0,0),labels = x_axis_labels, breaks = x_axis_labels) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
scale_fill_distiller(palette = "Reds", limits = c(0,10), na.value = "#de2d26",
direction = 1, labels = c(0.0, 2.5, 5.0, 7.5, "> 10.0")) +
labs(y= "Location", x = "Hour")
cc$location <- stri_trans_general(cc$location, "latin-ascii")
group_by(cc,day,location) %>% summarize(n=n()) %>%
ggplot(aes(x=factor(day, level=c('Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday')),location,fill=n)) + geom_tile() +
scale_fill_distiller(palette = "Reds", limits = c(0,10), na.value = "#de2d26",
direction = 1, labels = c(0.0, 2.5, 5.0, 7.5, "> 10.0")) +
labs(y= "Location", x = "Day") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5), panel.grid.major = element_blank())
cc$location <- stri_trans_general(cc$location, "latin-ascii")
group_by(cc,date,location) %>% summarize(n=n()) %>%
ggplot(aes(x=factor(date),location,fill=n)) + geom_tile() +
scale_fill_distiller(palette = "Reds", limits = c(0,10), na.value = "#de2d26",
direction = 1, labels = c(0.0, 2.5, 5.0, 7.5, "> 10.0")) +
labs(y= "Location", x = "Date") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5), panel.grid.major = element_blank())
popular_loc <- cc %>% group_by(day, location, hour) %>% summarise(n=n()) %>%
arrange(day,desc(n))
Importing raster file
bgmap <- raster("data/MC2-tourist.tif")
bgmap
class : RasterLayer
band : 1 (of 3 bands)
dimensions : 1595, 2706, 4316070 (nrow, ncol, ncell)
resolution : 3.16216e-05, 3.16216e-05 (x, y)
extent : 24.82419, 24.90976, 36.04499, 36.09543 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : MC2-tourist.tif
names : MC2.tourist
values : 0, 255 (min, max)
Plotting Raster Layer
tm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255)
Importing vector GIS data file
Abila_st <- st_read(dsn = "data/Geospatial",
layer = "Abila")
Reading layer `Abila' from data source
`C:\abmayur05\VisualAnalytics\_posts\2021-07-14-assignment\data\Geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 3290 features and 9 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 24.82401 ymin: 36.04502 xmax: 24.90997 ymax: 36.09492
Geodetic CRS: WGS 84
Importing Aspatial Data
gps <- read_csv("data/gps.csv")
glimpse(gps)
Rows: 685,169
Columns: 4
$ Timestamp <chr> "01/06/2014 06:28:01", "01/06/2014 06:28:01", "01/~
$ id <dbl> 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35~
$ lat <dbl> 36.07623, 36.07622, 36.07621, 36.07622, 36.07621, ~
$ long <dbl> 24.87469, 24.87460, 24.87444, 24.87425, 24.87417, ~
Converting Date-Time field and ID field
gps$Timestamp <- date_time_parse(gps$Timestamp,
zone = "",
format = "%m/%d/%Y %H:%M:%S")
gps$id <- as_factor(gps$id)
Converting Aspatial Data Into A Simple Feature data Frame
gps_sf <- st_as_sf(gps,
coords = c("long","lat"),
crs = 4326)
Separate date and hour
gps_sf$timestamp <- gps_sf$Timestamp
gps_sf <- gps_sf %>%
separate(Timestamp, c("date", "time"), " ")
gps_sf$day <- weekdays(as.Date(gps_sf$date))
gps_sf$date <- as.Date(gps_sf$date, "%Y-%m-%d")
gps_sf$hour <- as.POSIXct(gps_sf$time, format = "%H:%M")
gps_sf$hour <- as_hms(gps_sf$hour)
gps_sf$hour <- hour(gps_sf$hour)
Creating Movement Path From GPS Points
gps_path <- gps_sf %>%
group_by(id, date, hour) %>%
summarize(m= mean(timestamp),
do_union=FALSE) %>%
st_cast("LINESTRING")
Finding the orphan lines
p = npts(gps_path, by_feature = TRUE)
gps_path2 <- cbind(gps_path, p)
Removing the orphan lines
gps_path3 <- gps_path2[!(gps_path2$p==1),]
Plotting the GPS Paths
gps_path_selected <- gps_path3 %>%
filter(id==1)
tmap_mode("view")
tm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255) +
tm_shape(gps_path_selected) +
tm_lines()
tm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255) +
tm_shape(gps_path3) +
tm_lines()